Relative feature scores for brain parcels

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating explainability data that explains a medical condition in a subject. In one aspect, a method comprises: obtaining data identifying a plurality of brain parcels that are predicted to be relevant to the medical condition; receiving fMRI data for a brain of a subject; processing the fMRI data for the brain of the subject to determine a respective activation score for each of the plurality of brain parcels that are predicted to be relevant to the medical condition; determining, for each of the plurality of brain parcels that are predicted to be relevant to the medical condition, a relative activation score for the brain parcel; and taking an action based on the relative activation scores.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 17/521,687, filed Nov. 8, 2021, the entire contentsof which are incorporated herein by reference.

TECHNICAL FIELD

This specification relates to processing medical images.

BACKGROUND

Medical imaging includes techniques and processes for creating visualrepresentations of an interior of a body for clinical analysis andmedical intervention, as well as visual representation of physiology ofsome organs or tissues. Medical imaging can reveal internal structureshidden by skin and bones, and can be used to diagnose and treat variousdiseases. Medical imaging also can establish a database of normalanatomy and physiology to make it possible to identify abnormalitiesamongst cohorts. Some medical imaging can also provide insights intofunctional activity and structural connections of a brain.

SUMMARY

This specification generally describes technologies for processingmedical images of brains and/or displaying the processed images in auser-interactive brain navigation system (e.g., interface). Thedisclosed technology can be used by clinicians and other medicalprofessionals to glean insight about functional activity in a brain of asubject. Based on such insights, the clinicians and other medicalprofessionals can perform improved and more informed diagnoses,treatments, operations, and/or research than with existing systems.

Throughout this specification, a “subject” can refer to, e.g., apatient, human, animal, or other specimen. A “population” of subjectscan refer to a group of subjects.

A “parcel” in a brain refers to a region, e.g., a three-dimensional(3-D) volumetric region, of the brain. Typically, a parcel refers toregion that has a specified function, structural connectivity, orcellular composition. A collection of parcels that collectively define apartition of the brain may be referred to as a “parcel atlas.” A parcelatlas can include any appropriate number of parcels, e.g., 50 parcels,100 parcels, 500 parcels, or 1000 parcels.

Generally, a parcel atlas can be chosen such that each parcel in theparcel atlas is expected to have broadly similar properties (e.g.,functional activity, structural connectivity, or cellular composition)between subjects, even if the exact boundaries of the parcel differbetween subjects. A parcel atlas can be a useful mechanism for analyzingbrain images as it reduces the complexity of the brain architecture to afinite number of domains, which can be expected to play somewhat uniformroles in normal operation of the brain.

A brain can be imaged using a variety of possible imaging modalities.One such imaging modality is functional magnetic resonance imaging(fMRI). fMRI imaging of a brain over a sequence of time points yields asequence of fMRI images, where each fMRI image corresponds to arespective time point and characterizes blood flow and/or blood oxygenlevel at each voxel in the brain at the time point. Blood flow/bloodoxygen level in the brain is related to energy use by cells in thebrain, and thus fMRI imaging provides a method of characterizing neuralactivity in the brain over time.

Parcel atlases can be particularly useful when analyzing fMRI data. Forexample, fMRI imaging of a brain of a subject over a sequence of timepoints can associate each voxel in the brain with a “blood flow curve.”A blood flow curve associated with a voxel can characterize blood flowat location in the brain corresponding to the voxel at each time pointin the sequence of time points. The respective blood flow curvescorresponding to each voxel in a parcel can be averaged to generate an“average blood flow curve” for the parcel. A correlation between therespective average blood flow curves corresponding to two parcels in thebrain can be understood to represent a measure of functionalconnectivity between the two parcels in the brain of the subject.

Throughout this specification, a “medical condition” in a subject canrefer to a dysfunction or disorder in the subject. Examples of medicalconditions can include, e.g., psychiatric conditions, e.g.,schizophrenia, psychosis, or autism. In some cases, a medical conditioncan refer to a behavioral symptom, e.g., auditory hallucinations, visualhallucinations, or paranoia.

Particular embodiments of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages.

This specification describes a parcel scoring system that can processfMRI data characterizing neural activity in the brain of a subject,e.g., a subject having a medical condition, to generate data, referredto as “explainability data,” that “explains” the medical condition inthe subject. That is, the explainability data can provide interpretableinsight into the mechanism, cause, or state of the medical condition inthe subject. In particular, the explainability data includes a set of“relative activation scores.” Each relative activation score correspondsto a brain parcel and compares: (i) neural activity in the parcel in thebrain of the subject, and (ii) a distribution of neural activity in theparcel across a population of subjects. The parcel scoring system canprovide the explainability data to a user, e.g., by way of a userinterface, for use by the user, e.g., in determining appropriatediagnoses, treatments, or surgical procedures to treat the medicalcondition in the subject.

The parcel scoring system increases the interpretive value of therelative activation scores by providing (e.g., to the user) relativeactivation scores for only a fraction of the parcels in a parcel atlas,in particular, for those parcels that are predicted to be relevant tothe medical condition. Computing the relative activation scores for onlythe high-impact parcels significantly reduces the amount of data to beinterpreted by a user. Interpreting the relative activation scores forhundreds or thousands of parcels in a full parcel atlas would beunmanageable for many users, and thus significantly detract from theinterpretive value of the relative activation scores. Moreover, thehigh-impact parcels are precisely those parcels that are predicted to berelevant to the medical condition, and thus the relative activationscores for these parcels may provide the most interpretive value to theuser in explaining the medical condition in the target subject.

The parcel scoring system can also enable more efficient use ofcomputational resources (e.g., memory and computing power) by computingrelative activation scores for only those parcels in the parcel atlasthat are predicted to be relevant to the medical condition. To identifywhich parcels are relevant to the medical condition, the parcel scoringsystem can train a machine learning model to process fMRI data for aninput patient to predict whether the input patient has the medicalcondition. After training the machine learning model, the parcel scoringsystem can determine a respective importance score for each parcel thatmeasures the impact of the parcel on predictions generated by themachine learning model. The parcel scoring system can then use theimportance scores to identify the parcels that are most relevant to themedical condition. The parcel scoring system can thus leverage themachine learning model to accurately identify parcels relevant to themedical condition.

The details of one or more embodiments of the subject matter of thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example parcel scoring system.

FIG. 2 illustrates a flow of data between a user interface and a parcelscoring system.

FIG. 3 shows an example parcel identification system.

FIG. 4 illustrates an example distribution of importance scores for aset of parcels.

FIG. 5 illustrates an example of a user interface that graphicallyrepresents relative activation scores for high-impact parcels to a user,and enables the user to select a high-impact parcel to be displayed in a3-D model of the brain of a target subject.

FIG. 6 is a flow diagram of an example process for providing, to a user,a respective relative activation score for each brain parcel in a set ofbrain parcels that are predicted to be relevant to a medical condition.

FIG. 7 is a conceptual diagram illustrating a computing environment forgenerating a graphical user interface (GUI) representation of aparticular brain.

FIG. 8 illustrates components in a computing landscape that can be usedto generate the GUI representation of the particular brain.

FIG. 9 illustrates a user-interactive GUI of the particular brain.

FIG. 10 shows an example of a computing device and an example of amobile computing device.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

This specification describes a parcel scoring system that can processfMRI data characterizing neural activity in the brain of a subject,e.g., a subject having a medical condition, to generate explainabilitydata that explains the medical condition in the subject. In particular,the explainability data compares, for each of multiple parcels that arepredicted to be relevant to the medical condition: (i) neural activityin the parcel in the brain of the subject, and (ii) a distribution ofneural activity in the parcel across a population of subjects. Theexplainability data can thus provide an interpretation for, e.g., whichparcels in the brain of the subject are the greatest contributors to themedical condition in the subject. In the case of a subject that does nothave the medical condition, the explainability data can provide aninterpretation for, e.g., which parcels in the brain of the subjectprovide the most “protection” from the medical condition in the subject.

The parcel scoring system can provide the explainability data to a user,e.g., by way of a user interface on a user device. The user interfacecan enable the user to select one or more parcels in the brain of thesubject, e.g., in response to viewing the explainability data. Theparcel scoring system can perform a variety of possible actions inresponse to the selection, by the user, of one or more parcels in thebrain of the subject. For example, the parcel scoring system can provideanatomical data representing the location of the selected parcels in athree-dimensional (3-D) model of the brain of the subject, e.g., by wayof the user interface. As another example, the parcel scoring system canexport data identifying the selected parcels, e.g., to a data store, orto another system, e.g., a system used to perform a medical procedure.

An example parcel scoring system will be described in more detail belowwith reference to FIG. 1 .

The parcel scoring system can be implemented as part of a broadercomputer system that can optionally provide additional functionality andperform additional operations, e.g., involving processing medical imagesof brains and/or displaying the processed images.

An example computer system that can implement the parcel scoring systemwill be described in more detail below with reference to FIG. 7 .

FIG. 1 shows an example parcel scoring system 100. The parcel scoringsystem 100 is an example of a system implemented as computer programs onone or more computers in one or more locations in which the systems,components, and techniques described below are implemented.

The parcel scoring system 100 is configured to receive, e.g., from auser by way of a user interface made available on a user device, aselection of a medical condition 102. The user can select the medicalcondition 102, e.g., from a predefined set of medical conditions.

The parcel scoring system 100 also receives fMRI data 116 characterizingthe brain of a subject, referred to as the “target” subject 114. Theparcel scoring system 100 can receive the fMRI data 116 for the targetsubject 114, e.g., directly from a medical imaging device (e.g., an fMRIimaging device) that generated the fMRI data 116, or by retrieving thefMRI data 116 from a data store. The target subject 114 may be a subjectthat is understood (e.g., by clinical diagnosis) as being likely to havethe medical condition 102.

The parcel scoring system 100 includes a parcel identification system300, a comparison engine 106, and an action engine 110, which are eachdescribed in more detail next.

The parcel identification system 300 is configured to identify a set of(multiple) “high-impact” (or “high contribution” or “high protection”)brain parcels, from a parcel atlas, that are predicted to be relevant tothe medical condition 102. A brain parcel may be relevant to the medicalcondition 102 (i.e., and thus designated as a “high-impact” parcel forthe medical condition 102), e.g., if neural activity in the brain parcelis associated with the cause or mechanism of the medical condition in atleast some subjects. For example, a high-impact parcel 104 may be partof a malfunctioning neural circuit that contributes to the medicalcondition 102 in the target subject 114.

Generally, the parcel identification system 300 designates only afraction of the brain parcels in the parcel atlas as being high-impactbrain parcels 104 for the medical condition 102. For example, the parcelidentification system 300 can designate fewer than 50%, fewer than 20%,fewer than 10%, fewer than 5%, or fewer than 1% of the parcels in theparcel atlas as being high-impact parcels 104 for the medical condition102. In other implementations, the parcel identification system candesignate a fixed number (e.g., 5, 10 15, or 20) of high impact parcels.

The parcel identification system 300 can identify the set of high-impactparcels 104 for the medical condition 102 with reference to a machinelearning model that is configured to process fMRI data characterizing asubject to predict whether the subject has the medical condition. Inparticular, the parcel identification system 300 can identify a set ofparcels that have a significant impact on predictions generated by themachine learning model (i.e., for whether subjects have the medicalcondition), and thereafter designate the identified parcels as beinghigh-impact parcels for the medical condition. Example operations thatcan be performed by the parcel identification system 300 to identify theset of high-impact parcels 104 for the medical condition 102 withreference to a machine learning model are described in more detail withreference to FIG. 3 .

Generally, the set of high-impact parcels 104 associated with themedical condition 102 can be pre-computed. More specifically, the parcelidentification system 300 is not required to re-compute the set ofhigh-impact parcels 104 with reference to the machine learning modeleach time a user interacts with the parcel scoring system 100. Rather,the parcel identification system 300 can compute the high-impact parcels104 for the medical condition with reference to the machine learningmodel once, and then store data identifying the high-impact parcels 104for the medical condition 102 in a data store. Thereafter, in responseto receiving a user input selecting a medical condition 102, the parcelidentification system 300 can retrieve the high-impact parcels 104 forthe selected medical condition 102 from the data store, rather thanre-computing them.

The comparison engine 106 is configured to generate a score, referred toas “relative activation score” 108, for each high-impact parcel 104.Generally, the relative activation score 108 for a high-impact parcel104 compares neural activity in the high-impact parcel 104 of the targetsubject 114 to a distribution of neural activity in the high-impactparcel 104 across a population of subjects 120. The relative activationscores 108 provide explainability data that contributes to explainingthe medical condition 102 in the target subject 114, as will bedescribed in more detail below.

To generate the relative activation score 108 for a high-impact parcel104, the comparison engine 106 computes a respective “activation score”for the high-impact parcel 104 in: (i) the brain of the target subject,and (ii) the brain of each subject in a population of subjects 120. Thepopulation of subjects 120 can be, e.g., a population of subjects thathave the medical condition 102, or a population of subjects that do nothave the medical condition. (For convenience, the subjects in thepopulation of subjects may be referred to herein as “reference”subjects).

The comparison engine 106 can generate the activation score for a parcelin a brain, based on fMRI data characterizing the parcel in the brain,in any of a variety of possible ways. For example, the comparison engine106 can process fMRI data characterizing a parcel to determine themaximum value of the average blood flow curve for the parcel, andidentify the maximum value of the average blood flow curve as theactivation score for the parcel. As another example, the comparisonengine 106 can process fMRI data characterizing a parcel to determinethe average value of the average blood flow curve for the parcel, andidentify the average value of the average blood flow curve for theparcel as the activation score for the parcel. As another example, thecomparison engine 106 can process fMRI data characterizing a parcel todetermine the median value of the average blood flow curve for theparcel, and identify the median value of the average blood flow curvefor the parcel as the activation score for the parcel. As yet anotherexample, the comparison engine 106 can process fMRI data characterizinga parcel to determine blood oxygen level or the comparison engine canprocess EEG data characterizing a parcel to determine electricalactivity.

Thus, to generate the relative activation score 108 for a high-impactparcel 104, the comparison engine 106 processes fMRI data 116 for thetarget subject 114 to generate an activation score for the high-impactparcel 104 in the target subject 114. For each reference subject in thepopulation of reference subjects 120, the comparison engine 106processes fMRI data 122 for the reference subject 120 to generate anactivation score for the high-impact parcel 104 in the referencesubject. That is, the comparison engine 106 computes a distribution ofactivation scores for the high-impact parcel 104 across the populationof reference subjects 120 (i.e., where the “distribution” includes therespective activation score for the high-impact parcel for eachreference subject).

The comparison engine 106 generates the relative activation score 108for each high-impact parcel 104 based on: (i) the activation score forthe high-impact parcel 104 in the target subject 114, and (ii) thedistribution of activation scores for the high-impact parcel 104 acrossthe population of subjects 120. A few example techniques for generatingthe relative activation score 108 for a high-impact parcel 104 aredescribed next.

In one example, the comparison engine 106 can generate the relativeactivation score 108 for a high-impact parcel 104 as a ratio of: (i) theactivation score for the high-impact parcel 104 in the target subject114, and (ii) the average activation score for the high-impact parcel104 in the population of subjects 120.

Generally, the activation scores for each parcel in each subject in thepopulation of reference subjects 120 can be pre-computed. Morespecifically, the comparison engine 106 is not required to re-computethe activation scores for the parcels of the reference subjects in thepopulation of reference subjects 120 each time the comparison engine 106generates relative activation scores 108 for a target subject 114.Rather, the comparison engine 106 can compute the activation scores forthe parcels of the reference subjects once, and the store the activationscores in a data store. Thereafter, to compute the relative activationscores 108 for a target subject 114, the comparison engine 106 canretrieve the parcel activation scores for the population of referencesubjects 120 from the data store, i.e., rather than re-computing them.

The relative activation scores 108 compare neural activity in thehigh-impact parcels 104 of the target subject 114 to the distribution ofneural activity in the high-impact parcels 104 across the population ofreference subjects 120. In doing so, the relative activation scores 108provide explainability data that contributes to explaining the medicalcondition 102 in the target subject 114, e.g., by providing insight intothe mechanism and cause of the medical condition 102 in the targetsubject 114. In particular, the relative activation scores 108 canprovide insight into which parcels in the target subject 114 are likelyto be significant contributors to the medical condition 102 experiencedby the target subject 114.

For example, the relative activation score for a particular high-impactparcel may indicate that the activation score for the parcel in thetarget subject 114 differs significantly from the distribution ofactivation scores for the parcel across the population of subjects 120.In this example, a user may make an inference that neural activity inthe parcel is a contributor to the medical condition 102 in the targetsubject 114.

By computing the relative activation scores 108 for only the fraction ofthe parcels in the parcel atlas that are predicted to be relevant to themedical condition (i.e., the high-impact parcels 104), the parcelscoring system 100 increases the interpretive value of the relativeactivation scores 108. First, computing the relative activation scores108 for only the high-impact parcels 104 significantly reduces theamount of data to be interpreted by a user. Interpreting the relativeactivation scores for a hundred or more parcels in the full parcel atlaswould be unmanageable for many users, and thus significantly detractfrom the interpretive value of the relative activation scores. Second,the high-impact parcels are precisely those parcels that are predictedto be relevant to the medical condition, and thus the relativeactivation scores 108 for these parcels may provide the mostinterpretive value to the user in explaining the medical condition inthe target subject.

The action engine 110 is configured to provide the relative activationscores 108 to the user by way of the user interface on the user device.The relative activation scores 108 can be represented to the user on theuser interface in any of a variety of possible ways. For example, therelative activation scores 108 can be presented to the user graphically,e.g., as a bar graph, where each bar in the bar graph corresponds to arespective high-impact parcel and the height of the bar represents therelative activation score 108 for the high-impact parcel. FIG. 5 , whichwill be described in more detail below, illustrates an example of howthe relative activation scores can be presented to a user.

The user interface on the user device can enable the user to select oneor more of the high-impact parcels 104. For example, if the relativeactivation scores are presented to the user in the form of a bar graph(as described above), then the bars of the graph (and/or other elementsof the user interface) may be selectable (i.e., interactive) elements.In this example, the user can select one or more high-impact parcels 104by selecting interactive elements corresponding to those parcels in theuser interface.

After receiving user selection data 118, i.e., that selects one or moreof the high-impact parcels, the action engine 110 can perform any of avariety of possible actions based on the user selection data 118. A fewexamples of possible actions that can be performed by the action engine110 in response to receiving the user selection data 118 are describednext.

In one example, in response to receiving user selection data 118 thatselects a parcel, the action engine 110 can obtain anatomical data thatdefines a volumetric region in the brain of the target subject 114 thatcorresponds to the selected parcel. The anatomical data for the selectedparcel can be, e.g., data defining a polygonal mesh that encloses thevolumetric region of the brain corresponding to the selected parcel in a3-D model of the brain of the target subject 114. Anatomical data forthe parcels in the brain of the target subject 114 can be pre-computedand stored in a data store, and the action engine 110 can obtain theanatomical data for the selected parcel by retrieving it from the datastore.

After obtaining the anatomical data for the selected parcel, the actionengine 110 can provide the anatomical data for display to the user byway of the user interface on the user device. The anatomical data forthe selected parcel can be presented to the user in any of a variety ofpossible ways, e.g., as a polygonal mesh enclosing the volumetric regionof the brain corresponding to the selected parcel in a 3-D model of thebrain of the target subject.

In another example, to export a selected parcel, the action engine 110can store data identifying the selected parcel in a data store.

FIG. 2 illustrates a flow of data between: (i) a user interface 202,e.g., on a user device, and (ii) a parcel scoring system 100, e.g., asdescribed with reference to FIG. 1 .

The parcel scoring system 100 receives, by way of the user interface202, a selection (e.g., by a user of the user device) of a medicalcondition 102, e.g., from a predefined set of medical conditions. Theparcel scoring system 100 also receives fMRI data 116 for a targetsubject 114, e.g., a target subject 114 that has been diagnosed with themedical condition 102. The parcel scoring system 100 may receive thefMRI data 116 for the target subject 114 directly from a medical imagingdevice that captured the fMRI data 116, or the parcel scoring system 100may retrieve the fMRI data 116 from a data store.

The parcel scoring system 100 identifies one or more high-impact parcelsthat are predicted to be relevant to the medical condition 102, andprocesses the fMRI data 116 for the target subject to generate arespective relative activation score 108 for each high-impact parcel104. The relative activation score 108 for a high-impact parcel comparesneural activity in the high-impact parcel of the target subject 114 tothe distribution of neural activity in the high-impact parcel 104 acrossa population of subjects, e.g., a population of subjects that have themedical condition 102. The relative activation scores 108 provideexplainability data that can contribute to explaining the medicalcondition 102 in the target subject 114, e.g., by providing insight intothe mechanism and cause of the medical condition 102 in the targetsubject 114.

After generating the relative activation scores 108, the parcel scoringsystem 100 provides the relative activation scores 108 for display onthe user interface 202. The relative activation scores 108 can berepresented to the user by the user interface 202 in any of a variety ofpossible ways, e.g., graphically, as a bar graph, as illustrated withreference to FIG. 5 .

After the relative activation scores 108 are presented to the user onthe user interface 202, the user can interact with the user interface202 to select one or more of the high-impact parcels. The user selectiondata 118, i.e., identifying the high-impact parcels selected by theuser, is provided to the parcel scoring system 100.

In response to receiving the user selection data 118 that selects one ormore of the high-impact parcels, the parcel scoring system 100 canperform a variety of possible actions 112. For example, the parcelscoring system 100 can obtain anatomical data defining the locations ofthe selected parcels in the brain of the target subject 114, and providea visual representation 204 of the selected parcels to the user by wayof the user interface, e.g., as illustrated with reference to FIG. 5 .As another example, the parcel scoring system 100 can export 206 theselected parcels, e.g., by storing data identifying the selected parcelsin a data store.

FIG. 3 shows an example parcel identification system 300. The parcelidentification system 300 is an example of a system implemented ascomputer programs on one or more computers in one or more locations inwhich the systems, components, and techniques described below areimplemented.

The parcel identification system 300 is configured to receive dataidentifying a medical condition 102, and to generate data identifyingone or more parcels from a parcel atlas as being “high-impact” parcels,i.e., parcels that are predicted to be relevant to the medical condition102 or parcels that have the most different activity (high or low) insubjects with the condition relative to subjects without the condition.

The parcel identification system 300 identifies the high-impact parcelsusing a training engine 302, a machine learning model 304, an importancescoring engine 306, and a selection engine 310, which are each describedin more detail next.

The training engine 302 is configured to train the machine learningmodel 304 on a set of training data 316.

The machine learning model 304 is configured to process fMRI data 312characterizing the brain of a subject to generate a prediction 314 forwhether the subject has the medical condition 102.

The fMRI data 312 processed by the machine learning model 304 can berepresented in a variety of possible ways, i.e., prior to being providedfor processing by the machine learning model 304. For example, the fMRIdata 312 can be represented as a “functional connectivity matrix” havinga number of rows and columns equal to the number of parcels in theparcel atlas. The value at position (i, j) in the functionalconnectivity matrix can be defined as a correlation between theactivity, e.g., the average blood flow curves, corresponding to parcel iand parcel j in the brain of the subject. As another example, the fMRIdata 312 can be represented as a “functional connectivity vector” havinga number of entries equal the number of parcels in the parcel atlas.Each entry in the functional connectivity vector can be obtained bycombining, e.g., summing or averaging, a corresponding row or column ofthe functional connectivity matrix.

The prediction 314 generated by the machine learning model 304 candefine a predicted likelihood that the subject has the medicalcondition. In particular, the prediction 314 can be a numerical value,e.g., in the range [0,1], that defines a predicted likelihood of thesubject having the medical condition.

The machine learning model 304 can be any model having a set oflearnable parameters that can be trained to perform a prediction task.For example, the machine learning model can include, e.g., a neuralnetwork model, a random forest model, a support vector machine model, aboosted decision tree, or a combination thereof.

The training data 316 includes multiple training examples, where eachtraining example corresponds to a respective subject and includes: (i)fMRI data characterizing the brain of the subject, and (ii) a targetoutput that identifies whether the subject has the medical condition102. The training data 316 can include any appropriate number oftraining examples, e.g., 100 training examples, 1000 training examples,or 10,000 training examples. The training data 316 includes at leastsome training examples corresponding to subjects that have the medicalcondition 102, and at least some training examples corresponding tosubjects that do not have the medical condition 102.

Generally, for each training example in the training data 316, thetraining engine 302 trains the machine learning model 304 to process thefMRI data included in the training example to generate a prediction thatmatches the target output specified by the training example.

The training engine 302 can train the machine learning model 304 on thetraining data 316 using any appropriate training technique. For example,if the machine learning model 304 is a neural network model, then thetraining engine 302 can train the machine learning model 304 using astochastic gradient descent training technique.

The importance scoring engine 306 is configured to generate a respectiveimportance score 308 for each parcel in the parcel atlas, where theimportance score 308 for a parcel measures the impact of the parcel onpredictions generated by the (trained) machine learning model 304. Theimpact of a parcel on predictions generated by the machine learningmodel 304 can refer to, e.g., a scale of the change in predictionsgenerated by the machine learning model 304 that would result frommodifying the portion of the fMRI data characterizing neural activity inthe parcel.

The importance scoring engine 306 can generate the importance scores 308for the parcels in the parcel atlas using any appropriate technique.Example techniques for generating the importance scores are describedwith reference to: S. Doyen et al., “Hollow-tree Super: a directionaland scalable approach for feature importance in boosted tree models,”arXiv:2104.03088 (2021); S. M. Lundberg et al., “A unified approach tointerpreting model predictions,” arXiv:1705.07874v2 (2017); M. T.Ribeiro et al., “Why should I trust you: explaining the prediction ofany classifier,” Proceedings of the 22^(nd) ACM SIGKDD InternationalConference on Knowledge Discovery and Data Mining, pp. 1135-1144 (2016).

Generally, the importance score for a parcel can be understood as beingproportionate to the relevance of the parcel to the medical condition.That is, a parcel associated with a higher importance score can beunderstood as being more relevant to the medical condition, e.g.,because neural activity in that parcel has a higher impact onpredictions, generated by the machine learning, for whether subjectshave the medical condition. FIG. 4 illustrates an example distributionof importance scores for a set of parcels.

The selection engine 310 is configured to receive the importance scores308 for the parcels, and to designate a proper subset of the parcels inthe parcel atlas as being high-impact parcels 104 based on theimportance scores 308.

The selection engine 310 can select the high-impact parcels 104 from theparcel atlas based on the importance scores 308 of the parcels in theparcel atlas in any of a variety of ways. For example, the selectionengine 310 can designate any parcel having an importance score 308 thatsatisfies (e.g., exceeds) a predefined threshold value as being ahigh-impact parcel 104. As another example, the selection engine 310 candesignate a predefined number of parcels having the highest importancescores 308 as being high-impact parcels 104.

After determining the high-impact parcels 104 for the medical condition102, the parcel identification system 300 can provide data identifyingthe high-impact parcels 104 for the medical condition 102, e.g., to theparcel scoring system described with reference to FIG. 1 .

The parcel identification system 300 can determine high-impact parcels104 for a variety of medical conditions using the techniques describedabove. The set of high-impact parcels for one medical condition willtypically be different than the set of high-impact parcels for anothermedical condition, reflecting the diversity in underlying causes andmechanisms of medical conditions affecting the brain.

FIG. 4 illustrates an example distribution of importance scores for aset of parcels. In particular, for each parcel identified on thehorizontal axis, the height of the corresponding bar represents animportance score for the parcel that measures an impact of the parcel ofpredictions generated by the machine learning model for whether subjectshave a particular medical condition. It can be appreciated that themagnitude of the importance scores vary widely across the set ofparcels. The parcel scoring system (described with reference to FIG. 1 )computes relative activation scores (i.e., that compare neural activitybetween a target subject and a population of subjects) for only a propersubset of the parcels in a parcel atlas, e.g., for the parcels havingthe highest importance scores. The parcel scoring system thus increasesthe interpretability of the relative activation scores and theirrelevance to explaining a medical condition in the target subject.

FIG. 5 illustrates an example of a user interface that graphicallyrepresents relative activation scores for high-impact parcels to a user,and enables the user to select a high-impact parcel to be displayed in a3-D model of the brain of a target subject.

In particular, the user interface displays the relative activationscores for five high-impact parcels, i.e., parcel A 506, parcel B 508,parcel C 510, parcel D 512, and parcel E 514, in the brain of the targetsubject. The relative activation score for each high-impact parcel isrepresented by the length of a bar in a bar graph, and also as apercentage. For example, the user interface indicates that the relativeactivation score for parcel A is 140%, while the relative activationscore for parcel E is 20%.

The user interface enables a user to interact with the user interface toselect a high-impact parcel, e.g., the user can use a mouse 502 (oranother input device) to click 504 (or otherwise interact) with therepresentation of parcel B 508 on the user interface.

In response to the user selecting a high-impact parcel, the userinterface can display a 3-D model of the brain of the target patientthat illustrates the spatial location of the selected high-impact parcelin the brain of the brain target patient. For example, in response tothe user selecting parcel B 508, the user interface can display the 3-Dmodel 516 of the brain of the target patient that visually highlightsthe spatial location of parcel B 508.

FIG. 6 is a flow diagram of an example process 600 for providing, to auser, a respective relative activation score for each brain parcel in aset of brain parcels that are predicted to be relevant to a medicalcondition. For convenience, the process 600 will be described as beingperformed by a system of one or more computers located in one or morelocations. For example, a parcel scoring system, e.g., the parcelscoring system 100 of FIG. 1 , appropriately programmed in accordancewith this specification, can perform the process 600.

The system receives a selection of a medical condition (602). The usercan select the medical condition, e.g., from a predefined set of medicalconditions.

The system obtains data identifying a set of brain parcels that arepredicted to be relevant to the medical condition (604). The system mayhave previously identified the set of brain parcels that are predictedto be relevant to the medical condition by training a machine learningmodel to process an input derived from fMRI data for a brain of an inputsubject to predict whether the input subject has the medical condition.The system may then have identified the set of brain parcels that arepredicted to be relevant to the medical condition using the machinelearning model.

The system receives fMRI data for a brain of a subject (606). Forexample, the system can retrieve the fMRI data for the brain of thesubject from a data store.

The system processes the fMRI data for the brain of the subject todetermine a respective activation score for each of the brain parcelsthat are predicted to be relevant to the medical condition (608). Theactivation score for a brain parcel can characterize neural activity inthe brain parcel.

The system determines, for each of the brain parcels that are predictedto be relevant to the medical condition, a relative activation score forthe brain parcel based on: (i) the activation score for the brain parcelin the subject, and (ii) a distribution of activation scores for thebrain parcel across a population of subjects (610). For example, thesystem can determine the relative activation score for a brain parcelbased on a ratio of: (i) the activation score for the brain parcel inthe subject, and (ii) a measure of central tendency of the distributionof activation scores for the brain parcel across the population ofsubjects.

The system provides, to a user device of a user, the relative activationscores for the brain parcels that are predicted to be relevant to themedical condition as explainability data that explains the medicalcondition in the subject (612).

The system takes an action based on the relative activation scores(614). For example, after providing the relative activation scores tothe user device of the user, the system can receive selection data fromthe user that selects one or more of the brain parcels that arepredicted to be relevant to the medical condition. The system can thentake one or more actions in response to receiving the selection datafrom the user, e.g., displaying anatomical data identifying thelocations of the selected parcels in a 3-D model of the brain of thesubject.

FIG. 7 is a conceptual diagram illustrating a computing environment 700for generating a GUI representation of a particular brain. The computingenvironment 700 can include a user device 704, a computer system 706, adata store 708, and a medical imaging device 710, which can communicate(e.g., wired and/or wirelessly) via network(s) 702.

The user device 704 can be used by a medical professional, such as aclinician, surgeon, doctor, nurse, researcher, or other professional.The user device 704 and technologies described herein can be used by anyother user. The user device 704 can be any one of a computer, laptop,tablet, mobile device, mobile phone, and/or smartphone. Sometimes, theuser device 704 can be integrated into or otherwise part of one or moreother devices in a medical setting, such as the medical imaging device710 and/or the computer system 706. The medical professional can use theuser device 704 to view information about a patient's brain. Forexample, using the disclosed technology, the medical professional canview, at the user device 704, 3D representations of a particularpatient's brain and make determinations about what diagnosis, treatment,and/or surgical procedures to perform. The medical professional can alsoview other/additional information about the particular patient at theuser device 704 to make more informed decisions with regards to theparticular patient's diagnosis, treatment, surgery, or other medical orresearch purposes. Thus, the user device 704 can provide hardware thatcan support the GUIs, software, and applications described herein, suchas a singular and interactive brain navigation system that makes iteasier and more intuitive for the medical professionals to make medicaland research determinations.

The computer system 706 can be a remote computing system, a cloud-basedsystem or service, and/or integrated with or otherwise part of one ormore devices in a medical setting (e.g., such as the user device 704and/or the medical imaging device 710). The computer system 706 can be acomputer, processor, a network of computers, a server, and/or a networkof servers. Sometimes, each medical setting (e.g. a hospital) can haveone or more computer systems 706. Sometimes, the computer system 706 canbe used across multiple medical settings (e.g., multiple hospitals). Thecomputer system 706 can be configured to generate interactiverepresentations of patients' brains based off image data of the brains.The computer system 706 can also generate GUIs to display theinteractive representations of the brains at the user device 704.

Sometimes, the computer system 706 can clean the image data by removingpersonally identifying information (e.g., protected health information(PHI)) from that data. Cleaning the image data can be beneficial topreserve patient privacy, especially if the interactive representationsof patients' brains are used for medical research, clinical studies, orotherwise are stored in the data store 708 for future retrieval and use.Removing personally identifying information can also be advantageous ifthe computer system 706 is remote from the user device 704 and theinteractive representations of the brain are generated at the computersystem 706 that is outside a secure hospital infrastructure or othernetwork where the image data may be generated and/or the interactiverepresentations of the brain may be outputted. In other words, removingpersonally identifying information can be advantageous to preservepatient privacy when patient data is communicated between differentnetworks and/or infrastructure.

The computer system 706 can implement the parcel scoring system 100described with reference to FIG. 1 . The computer system 706 canimplement the operations of the parcel scoring system 100, as analternative to or in combination with the other operations describedwith reference to FIG. 7 .

The data store 708 can be a remote data store, cloud-based, orintegrated into or otherwise part of one or more other components in themedical setting (e.g., such as the user device 704 and/or the computersystem 706). The data store 708 can store different types ofinformation, including but not limited to image data of patient brains(e.g., from the medical imaging device 710), cleaned image data (e.g.,from the computer system 706), 3D representations of patient brains orother interactive representations of patient brains (e.g., from thecomputer system 706), connectivity data associated with patient brains,determinations, actions, or other user input taken by the medicalprofessional (e.g., at the user device 704), patient information orrecords, or other relevant information that can be used in a medicalsetting.

The medical imaging device 710 can be any type of device and/or systemthat is used in the medical setting to capture image data of patientbrains. The medical imaging device 710 can capture image data thatincludes but is not limited to x-rays, computed tomography (CT) scans,magnetic resonance imaging (Mills), electroencephalography (EEG) and/orultrasound. One or more other types of image data can also be capturedby the medical imaging device 710. The computer system 706 can beconfigured to receive any type of image data of a patient's brain andglean connectivity data about the brain from that image data to map thedata onto a user-friendly interactive representation of a brain.

Still referring to FIG. 1 , the computer system 706 can receive imagedata of a patient's brain from one or more of the user device 704 (stepA1), the medical imaging device 710 (step A2), and the data store 708(step A3). Sometimes, for example, when the user device 704 is part ofthe medical imaging device 710, the computer system can receive theimage data captured by the medical imaging device 710 from only onedevice (e.g., the medical imaging device 710 or the user device 704).The image data can be captured by the medical imaging device 710 thensent directly, in real-time, to the computer system 706 (step A2) forreal-time processing. Sometimes, the image data can be captured by themedical imaging device 710, then initially reviewed by the medicalprofessional at the user device 704. Accordingly, the user device 704can transmit the image data to the computer system 706 (step A1).

In some implementations, image data can be captured of multipledifferent brains by multiple different medical imaging devices 710. Theimage data can be stored in the data store 708 for future processing andanalysis. The computer system 706 can then retrieve a batch or batchesof the image data from the data store 708 (step A3) and process theimage data in batch. Processing in batch can be advantageous to usefewer computational resources and reduce network bandwidth.

Once the computer system 706 receives the image data (steps A1-A3), thecomputer system can generate a model of the brain using a representationof a brain (step B). For example, the computer system 706 can map ormodel the patient's brain from the image data onto a 3D representationof a brain. The 3D representation can be a generic brain in3-dimensional or other multi-dimensional space. The 3D representationcan be a glass brain. Mapping the patient's brain onto the glass braincan be advantageous to provide vantage points of different structures,parcels, and connectivity in the particular patient's brain. A medicalprofessional can more easily analyze the particular patient's brain viathe 3D representation of the brain rather than through the raw imagedata captured by the medical imaging device 710. As a result, themedical professional can generate more informed decisions anddeterminations with regards to the particular patient's diagnosis,treatment, surgery, condition, or other medical or research purposes.

Once the patient's brain is modeled using the representation of thebrain (step B), the computer system 706 can output the model of thepatient's brain in a GUI at the user device 704 (step C). For example,the computer system 706 can generate the GUI that displays the model ofthe patient's brain, then transmit the GUI to the user device 704 to beoutputted. The model can represent the patient's brain overlaid on theglass brain. Sometimes, instead of outputting the model at the userdevice 704 (step C), the computer system 706 can store the model of thepatient's brain in the data store 708. The model of the patient's braincan then be accessed/retrieved at a later time and presented to themedical professional or other user at the user device 704.

As mentioned throughout, when the model of the patient's brain isoutputted at the user device 704, the GUI can allow the user, e.g., amedical professional, to take numerous actions in response to reviewingthe model of the patient's brain. For example, the medical professionalcan determine what type of diagnosis, treatment, or surgical proceduresto take with regards to this particular patient. The medicalprofessional can also interact with the model of the patient's brainthrough use-selectable options and features in the GUI that is outputtedat the user device 704. The medical professional can change views of themodel of the patient's brain (e.g., rotate around the model, view only aleft or right side of the patient's brain, etc.), select portions of thepatient's brain from the model (e.g., select a particular lobe, node,parcel, etc.), view other information about the patient (e.g., healthrecords, prior medical visits, etc.), and simulate surgical proceduresthat can impact different parcels or portions of the patient's brain(e.g., slicing a node or nodes that are connected to other nodes in thepatient's brain). The medical professional can provide input to the userdevice 704, for example, via an input device, and the input can indicatethe medical professional's interaction(s) with the model of thepatient's brain. This input can then be received by the computer system706 (step D).

The computer system 706 can take an action based on the received userinput (step E). For example, if the medical professional changes orselects a different view of the model of the patient's brain, then thecomputer system 706 can generate an updated GUI display of the patient'sbrain that only includes the selected view. This updated GUI display canbe outputted at the user device (step F). As another example, themedical professional can remove one or more nodes from the model of thepatient's brain. The computer system 706 can receive this input (stepD), simulate removal of the user-selected nodes (step E), then outputresults of removing such nodes from the brain at the user device 704(step F). The medical professional can review the outputted results andtake further actions in response. Further actions can include decisionsabout what nodes the medical professional should remove during theactual medical procedure and/or how to proceed with diagnosis,treatment, and/or the medical procedure.

Sometimes, the computer system 706 can take an action based on the userinput (step E) that does not also include outputting a result of theaction at the user device 704 (step F). For example, the medicalprofessional can input notes about what actions the medical professionalintends to take during a medical procedure, a diagnosis for theparticular patient, and/or treatment for the patient. The computersystem 706 can receive this input and store it in the data store 708 butmay not output results from storing this input. This input can then beretrieved from the data store 708 and provided to one or more otherdevices (e.g., a report can be generated that indicates the patient'sdiagnosis and treatment). The report can then be provided to a device ofthe patient. The report can also be transmitted to devices of othermedical professionals, such as those in a hospitalinfrastructure/network). The computer system 706 can take one or moreother actions based on the user input (step E) and optionally outputresults of the action(s) at the user device 704 (step F).

FIG. 8 illustrates components in a computing landscape that can be usedto generate the GUI representation of the particular brain. As describedabove, the user device 104, computer system 706, data store 708, andmedical imaging device 710 can communicate via the network(s) 102. Oneor more of the components 104, 706, 708, and 710 can also be integratedinto a same computing system, network of devices, server, cloud-basedservice, etc. The network(s) 102 may be a wide-area network (WAN), suchas the Internet, a cellular telecommunications network, or a privateWAN. Connection via the network(s) 102 can include a traditional dial-upmodem, a high-capacity (e.g., cable) connection such as a broadbandmodem, and/or a wireless modem.

The computer system 706 can include processor(s) 802, communicationinterface 804, brain modelling engine 806, GUI generation engine 808,and the parcel scoring system 100 described with reference to FIG. 1 .The processor(s) 802 can be configured to perform one or more operationsdescribed herein. Although not depicted, the computer system 706 canalso include at least one memory unit, which may have semiconductorrandom access memory (RAM) and semiconductor read only memory (ROM).

One or more of the techniques and processes described herein can beimplemented as software application programs executable by theprocessor(s) 802 in the computer system 706. Moreover, one or more ofthe techniques and processes described herein can be executed inbrowsers at remote terminals, systems, or devices (e.g., the user device104 and/or another computer system), thereby enabling a user of theremote terminals, systems, or devices to access the software applicationprograms that are executing on the computer system 706. For example,steps for any of the techniques and processes described herein can beeffected by instructions in the software application programs that arecarried out within the computer system 706. Software instructions may beformed as one or more code modules (e.g., using PYTHON or equivalentlanguage modules installed on the computer system 706 and/or the remoteterminals, systems, or devices), each for performing one or moreparticular tasks. The software instructions can also be divided intoseparate parts. For example, a first part and the corresponding codemodule(s) can perform the techniques and processes described herein anda second part and the corresponding code module(s) can manage a userinterface (e.g., the GUIs described herein) between the first part andthe medical professional at the user device 104.

Moreover, the software may be stored in a non-transitory, tangible,computer readable medium, including storage devices described throughoutthis disclosure. The software can be loaded into the computer system 706from the computer readable medium, and then executed by the computersystem 706. A computer readable medium having such software or computerprogram recorded on the computer readable medium can be a computerprogram product. Examples of transitory or non-tangible computerreadable transmission media that may also participate in the provisionof software, application programs, instructions and/or data includeradio or infra-red transmission channels as well as a network connectionto another computer or networked device, and the Internet or Intranets,including e-mail transmissions and information recorded on Websites andthe like.

Still referring to the computer system 706, the brain modelling engine806 can be configured to map a patient's brain onto a representation ofa brain (e.g., refer to step B in FIG. 1 ). For example, the brainmodelling engine 806 can receive patient brain image data 810A-N, whichcan be used to generate a model of the patient's brain. The patientbrain image data 810A-N can be received from the medical imaging device710. The patient brain image data 810A-N can also be received from theuser device 104. In some implementations, as described in reference toFIG. 1 , the computer system 706 can retrieve patient brain image data812A-N from the data store 708. The patient brain image data 812A-N canthen be used by the brain modelling engine 806 to model the patient'sbrain.

Sometimes, modelling the brain can include identifying connectivity datafor the particular brain. Modelling the brain can then include mappingthe connectivity data over the representation of a generic brain. In yetsome implementations, modelling the patient's brain can includeidentifying hubs, parcels, deep nodes, lateral nodes, and other portionsof the patient's brain that can be mapped onto the representation of thegeneric brain. Moreover, the brain modelling engine 806 can beconfigured to identify personally identifying information in the imagedata of the brain and extract that information before mapping thepatient's brain onto the representation of the generic brain. The brainmodelling engine 806 can use one or more machine learning models toaccurately map the particular patient's brain data onto a representationof the generic brain.

In some implementations, for example, Digital Imaging and Communicationsin Medicine (DICOM) images of a particular brain to be parcellated canbe processed by the brain modelling engine 806. DICOM is aninternational standard for transmitting, storing, retrieving, processingand/or displaying medical imaging information. A registration functionfor the particular brain can be determined in a Montreal NeurologicalInstitute (MNI) space (a common coordinate space) described by a set ofstandard brain data image sets, a registered atlas from a humanconnectome project can be determined, and diffusion tractography of theDICOM images can be performed to determine a set of whole braintractography images of the particular brain (in neuroscience,tractography can be thought of as a 3D modelling technique used torepresent white matter tracts visually). For each voxel in a particularparcel in the registered atlas, voxel level tractography vectors showingconnectivity of the voxel with voxels in other parcels can bedetermined, the voxel can be classified based on the probability of thevoxel being part of the particular parcel, and determining of the voxellevel tractography vectors and the classifying of the voxels for allparcels of the HCP-MMP1 Atlas can be repeated to form a personalisedbrain atlas (PBs Atlas) containing an adjusted parcel scheme reflectingthe particular brain.

The GUI generation engine 808 can be configured to generate GUI displaysof the modelled brain. The GUI generation engine 808 can receive themodelled brain from the brain modelling engine 806 and generate anappropriate GUI for displaying the modelled brain to the medicalprofessional (e.g., refer to FIG. 3 ). The GUI generation engine 808 canalso transmit the generated GUI(s) to the user device 104 to beoutputted/presented to the medical professional.

Moreover, whenever user input is received from the user device 104 thatincludes performing some action in response to the outputted model ofthe brain, the input can be received by the computer system 706. Thebrain modelling engine 806 can take some action (e.g., refer to step Ein FIG. 1 ) in response to receiving the user input (e.g., refer to stepD in FIG. 1 ). That action can include, for example, simulating removalof one or more nodes in the patient's brain. The GUI generation engine808 can generate updated GUI displays based on the actions taken by thebrain modelling engine 806 (e.g., refer to step F in FIG. 1 ). The GUIgeneration engine 808 can then transmit the updated GUI displays to theuser device 104 to be outputted to the medical professional.

Sometimes, one or more of the components of the computer system 706,such as the brain modelling engine 806 and the GUI generation engine 808can be part of one or more different systems. For example, the brainmodelling engine 806 can be part of a software application program thatcan be loaded and/or executed at another device, such as the user device104 and/or the medical imaging device 706. As another example, the GUIgeneration engine 808 can be part of a software application program thatis executed at the user device 104 and the brain modelling engine 806can be executed at the computer system 706 or another remote computingsystem, server, or cloud-based server or system.

The user device 104 can include processor(s) 814, input device(s) 816,output device(s) 818, application interface 820, and communicationinterface 822. The processor(s) 814 can be configured to perform one ormore operations described herein. Although not depicted, the user device104 can also include at least one memory unit, which may havesemiconductor random access memory (RAM) and semiconductor read onlymemory (ROM).

The input device(s) 816 and output device(s) 818 can include one or moreof an audio-video interface that couples to a video display, speakers,and/or a microphone, keyboard, mouse, scanner, camera, touch screendisplay, other display screen(s) (e.g., LCDs), joystick, and/or otherhuman interface device. The input device(s) 816 can be configured toreceive user input from the medical professional or other user. Theoutput device(s) 818 can be configured to output the model of thepatient's brain and/or actions taken by the computer system 706 inresponse to the user input. The output device(s) 818 can present avariety of GUI displays and information to the medical professional,where such displays and information are generated by the computer system706. The output device(s) 818 can also output information that isreceived or otherwise generated by the medical imaging device 710.

The application interface 820 can be executable software or anotherprogram that is deployed at the user device 104. The GUIs generated bythe computer system 706 can be displayed or otherwise outputted via theapplication interface 820. In some implementations, the applicationinterface 820 can be executed at a browser of the user device 104. Themedical professional can then access and view the GUIs via the Internetor other connection. Sometimes, the application interface 820 can beexecuted as a software module/program/product at the user device 104.The application interface 820 can provide the interactive GUIs to themedical professional and receive input from the medical professional(e.g., refer to FIG. 3 ).

The communication interfaces 804 and 822 can be configured to providecommunication between and amongst the components described herein. Forexample, a modem can be integrated therein.

FIG. 9 illustrates a user-interactive GUI 900 of the particular brain.The GUI 900 can be outputted at the user device 104 described herein.The GUI 900 outputs processed medical imaging data that is received ofthe particular brain. For example, the GUI 900 can include processeddata 902, patient information 904, and selectable options 906. Theprocessed data 902 can include the particular brain as it is modeled ona representation of a brain. For example, the processed data 902 caninclude a 3D representation of the particular brain, such as theparticular brain overlaying a glass brain. The processed data 902 alsomay not include other information that can appear in imaging data, suchas patient information or other PHI. The PHI that corresponds to theprocessed data 902 can optionally be outputted in the patientinformation 904.

The GUI 900 can provide a brain navigation system that is configured todisplay visual representations of an interior of the particular brainfor clinical analysis and medical intervention, as well as visualrepresentation of physiology of specific portions or objects of thebrain (e.g. tracts, hubs, or parcels of the brain). Such visualrepresentations can reveal internal structures hidden by the skin andbones, and can be used to diagnose and treat various different diseases.

The medical professional can use the selectable options 906 to specifyparticular actions (e.g. by making selections in the GUI 900 presentedat the user device 104) that the medical professional would like to takewith regards to the processed data 902. The medical professional canalso choose options to export the processed data within an IT network ofthe hospital or other medical setting where the medical professionalworks. The medical professional can save the exported data (e.g., in thedata store 108 in FIG. 1 ), which can be used in future research andanalysis.

The GUI 900 presents only some options that may be presented to themedical professional with regards to the processed data 902. One or moreother options are also possible and can be presented in the GUI 900and/or in additional GUIs that are outputted at the user device 104.

Moreover, as described herein, the GUI 900 can be part of a specializedcomputing system in the hospital IT infrastructure. Sometimes, the GUI900 can also be accessible via a web browser. The GUI 900 may beconfigured—e.g. by authentication mechanisms such as login usingusername and/or password, biometric detection, and/or the like—to beused by only authorized individuals, such as clinicians (e.g. doctors,nurses, clinical staff, or the like), other medical professionals, orother authorized users (e.g. network administrator, technical staff, orthe like) at the hospital or other medical setting. In someimplementations, the GUI 900 can also be in communication with orotherwise linked to one or more external devices, such as remotecomputers, that can be used to facilitate brain surgery or other medicalprocedures.

Although a brain image is useful for a medical professional, the medicalprofessional can benefit more if they have additional information aboutcomponents of the brain that is imaged. This additional information canbe advantageous for the medical professional to make more informeddecisions with regard to diagnosis, treatment, and medical procedures.Accordingly, as shown in FIG. 3 , the GUI 900 can provide the medicalprofessional with tools (e.g., such as the selectable options 906) thatallow the medical professional to interact with the modelled version ofthe particular brain. The medical professional can provide input forselecting portions of the processed data 902. The selected portions canbe objects—e.g. brain tracts and/or brain parcels that the medicalprofessional desires to see more information about, remove from thebrain in a simulated procedure, or otherwise review and analyze.Accordingly, the medical professional can specify particular portions ofthe brain to analyze. The medical professional may also desire toidentify and specify, on the GUI 900, particular objects on severalfeatures, such as local properties of brain tissue, long-rangeconnectivity patterns, structural markers, functional markers, and/orthe like. The disclosed technology therefore can provide the medicalprofessional with a more comprehensive, interactive, and user friendlyinterface for making determinations about a particular patient's braincondition(s).

FIG. 10 shows an example of a computing device 1000 and an example of amobile computing device that can be used to implement the techniquesdescribed here. The computing device 1000 is intended to representvarious forms of digital computers, such as laptops, desktops,workstations, personal digital assistants, servers, blade servers,mainframes, and other appropriate computers. The mobile computing deviceis intended to represent various forms of mobile devices, such aspersonal digital assistants, cellular telephones, smart-phones, andother similar computing devices. The components shown here, theirconnections and relationships, and their functions, are meant to beexemplary only, and are not meant to limit implementations of theinventions described and/or claimed in this document.

The computing device 1000 includes a processor 1002, a memory 1004, astorage device 1006, a high-speed interface 1008 connecting to thememory 1004 and multiple high-speed expansion ports 1010, and alow-speed interface 1012 connecting to a low-speed expansion port 1014and the storage device 1006. Each of the processor 1002, the memory1004, the storage device 1006, the high-speed interface 1008, thehigh-speed expansion ports 1010, and the low-speed interface 1012, areinterconnected using various busses, and can be mounted on a commonmotherboard or in other manners as appropriate. The processor 1002 canprocess instructions for execution within the computing device 1000,including instructions stored in the memory 1004 or on the storagedevice 1006 to display graphical information for a GUI on an externalinput/output device, such as a display 1016 coupled to the high-speedinterface 1008. In other implementations, multiple processors and/ormultiple buses can be used, as appropriate, along with multiple memoriesand types of memory. Also, multiple computing devices can be connected,with each device providing portions of the necessary operations (e.g.,as a server bank, a group of blade servers, or a multi-processorsystem).

The memory 1004 stores information within the computing device 1000. Insome implementations, the memory 1004 is a volatile memory unit orunits. In some implementations, the memory 1004 is a non-volatile memoryunit or units. The memory 1004 can also be another form ofcomputer-readable medium, such as a magnetic or optical disk.

The storage device 1006 is capable of providing mass storage for thecomputing device 1000. In some implementations, the storage device 1006can be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. A computer program product can be tangibly embodied inan information carrier. The computer program product can also containinstructions that, when executed, perform one or more methods, such asthose described above. The computer program product can also be tangiblyembodied in a computer- or machine-readable medium, such as the memory1004, the storage device 1006, or memory on the processor 1002.

The high-speed interface 1008 manages bandwidth-intensive operations forthe computing device 1000, while the low-speed interface 1012 manageslower bandwidth-intensive operations. Such allocation of functions isexemplary only. In some implementations, the high-speed interface 1008is coupled to the memory 1004, the display 1016 (e.g., through agraphics processor or accelerator), and to the high-speed expansionports 1010, which can accept various expansion cards (not shown). In theimplementation, the low-speed interface 1012 is coupled to the storagedevice 1006 and the low-speed expansion port 1014. The low-speedexpansion port 1014, which can include various communication ports(e.g., USB, Bluetooth, Ethernet, wireless Ethernet) can be coupled toone or more input/output devices, such as a keyboard, a pointing device,a scanner, or a networking device such as a switch or router, e.g.,through a network adapter.

The computing device 1000 can be implemented in a number of differentforms, as shown in the figure. For example, it can be implemented as astandard server 1020, or multiple times in a group of such servers. Inaddition, it can be implemented in a personal computer such as a laptopcomputer 1022. It can also be implemented as part of a rack serversystem 1024. Alternatively, components from the computing device 1000can be combined with other components in a mobile device (not shown),such as a mobile computing device 1050. Each of such devices can containone or more of the computing device 1000 and the mobile computing device1050, and an entire system can be made up of multiple computing devicescommunicating with each other.

The mobile computing device 1050 includes a processor 1052, a memory1064, an input/output device such as a display 1054, a communicationinterface 1066, and a transceiver 1068, among other components. Themobile computing device 1050 can also be provided with a storage device,such as a micro-drive or other device, to provide additional storage.Each of the processor 1052, the memory 1064, the display 1054, thecommunication interface 1066, and the transceiver 1068, areinterconnected using various buses, and several of the components can bemounted on a common motherboard or in other manners as appropriate.

The processor 1052 can execute instructions within the mobile computingdevice 1050, including instructions stored in the memory 1064. Theprocessor 1052 can be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 1052can provide, for example, for coordination of the other components ofthe mobile computing device 1050, such as control of user interfaces,applications run by the mobile computing device 1050, and wirelesscommunication by the mobile computing device 1050.

The processor 1052 can communicate with a user through a controlinterface 1058 and a display interface 1056 coupled to the display 1054.The display 1054 can be, for example, a TFT (Thin-Film-Transistor LiquidCrystal Display) display or an OLED (Organic Light Emitting Diode)display, or other appropriate display technology. The display interface1056 can comprise appropriate circuitry for driving the display 1054 topresent graphical and other information to a user. The control interface1058 can receive commands from a user and convert them for submission tothe processor 1052. In addition, an external interface 1062 can providecommunication with the processor 1052, so as to enable near areacommunication of the mobile computing device 1050 with other devices.The external interface 1062 can provide, for example, for wiredcommunication in some implementations, or for wireless communication inother implementations, and multiple interfaces can also be used.

The memory 1064 stores information within the mobile computing device1050. The memory 1064 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 1074 can also beprovided and connected to the mobile computing device 1050 through anexpansion interface 1072, which can include, for example, a SIMM (SingleIn Line Memory Module) card interface. The expansion memory 1074 canprovide extra storage space for the mobile computing device 1050, or canalso store applications or other information for the mobile computingdevice 1050. Specifically, the expansion memory 1074 can includeinstructions to carry out or supplement the processes described above,and can include secure information also. Thus, for example, theexpansion memory 1074 can be provide as a security module for the mobilecomputing device 1050, and can be programmed with instructions thatpermit secure use of the mobile computing device 1050. In addition,secure applications can be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory can include, for example, flash memory and/or NVRAM memory(non-volatile random access memory), as discussed below. In someimplementations, a computer program product is tangibly embodied in aninformation carrier. The computer program product contains instructionsthat, when executed, perform one or more methods, such as thosedescribed above. The computer program product can be a computer- ormachine-readable medium, such as the memory 1064, the expansion memory1074, or memory on the processor 1052. In some implementations, thecomputer program product can be received in a propagated signal, forexample, over the transceiver 1068 or the external interface 1062.

The mobile computing device 1050 can communicate wirelessly through thecommunication interface 1066, which can include digital signalprocessing circuitry where necessary. The communication interface 1066can provide for communications under various modes or protocols, such asGSM voice calls (Global System for Mobile communications), SMS (ShortMessage Service), EMS (Enhanced Messaging Service), or MMS messaging(Multimedia Messaging Service), CDMA (code division multiple access),TDMA (time division multiple access), PDC (Personal Digital Cellular),WCDMA (Wideband Code Division Multiple Access), CDMA2000, or GPRS(General Packet Radio Service), among others. Such communication canoccur, for example, through the transceiver 1068 using aradio-frequency. In addition, short-range communication can occur, suchas using a Bluetooth, WiFi, or other such transceiver (not shown). Inaddition, a GPS (Global Positioning System) receiver module 1070 canprovide additional navigation- and location-related wireless data to themobile computing device 1050, which can be used as appropriate byapplications running on the mobile computing device 1050.

The mobile computing device 1050 can also communicate audibly using anaudio codec 1060, which can receive spoken information from a user andconvert it to usable digital information. The audio codec 1060 canlikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 1050. Such sound caninclude sound from voice telephone calls, can include recorded sound(e.g., voice messages, music files, etc.) and can also include soundgenerated by applications operating on the mobile computing device 1050.

The mobile computing device 1050 can be implemented in a number ofdifferent forms, as shown in the figure. For example, it can beimplemented as a cellular telephone 1080. It can also be implemented aspart of a smart-phone 1082, personal digital assistant, or other similarmobile device.

This specification uses the term “configured” in connection with systemsand computer program components. For a system of one or more computersto be configured to perform particular operations or actions means thatthe system has installed on it software, firmware, hardware, or acombination of them that in operation cause the system to perform theoperations or actions. For one or more computer programs to beconfigured to perform particular operations or actions means that theone or more programs include instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the operations oractions.

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Embodiments of the subject matter described in thisspecification can be implemented as one or more computer programs, i.e.,one or more modules of computer program instructions encoded on atangible non-transitory storage medium for execution by, or to controlthe operation of, data processing apparatus. The computer storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofone or more of them. Alternatively or in addition, the programinstructions can be encoded on an artificially-generated propagatedsignal, e.g., a machine-generated electrical, optical, orelectromagnetic signal, that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus.

The term “data processing apparatus” refers to data processing hardwareand encompasses all kinds of apparatus, devices, and machines forprocessing data, including by way of example a programmable processor, acomputer, or multiple processors or computers. The apparatus can alsobe, or further include, special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application-specificintegrated circuit). The apparatus can optionally include, in additionto hardware, code that creates an execution environment for computerprograms, e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, or acombination of one or more of them.

A computer program, which may also be referred to or described as aprogram, software, a software application, an app, a module, a softwaremodule, a script, or code, can be written in any form of programminglanguage, including compiled or interpreted languages, or declarative orprocedural languages; and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A program may, but neednot, correspond to a file in a file system. A program can be stored in aportion of a file that holds other programs or data, e.g., one or morescripts stored in a markup language document, in a single file dedicatedto the program in question, or in multiple coordinated files, e.g.,files that store one or more modules, sub-programs, or portions of code.A computer program can be deployed to be executed on one computer or onmultiple computers that are located at one site or distributed acrossmultiple sites and interconnected by a data communication network.

In this specification the term “engine” is used broadly to refer to asoftware-based system, subsystem, or process that is programmed toperform one or more specific functions. Generally, an engine will beimplemented as one or more software modules or components, installed onone or more computers in one or more locations. In some cases, one ormore computers will be dedicated to a particular engine; in other cases,multiple engines can be installed and running on the same computer orcomputers.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby special purpose logic circuitry, e.g., an FPGA or an ASIC, or by acombination of special purpose logic circuitry and one or moreprogrammed computers.

Computers suitable for the execution of a computer program can be basedon general or special purpose microprocessors or both, or any other kindof central processing unit. Generally, a central processing unit willreceive instructions and data from a read-only memory or a random accessmemory or both. The essential elements of a computer are a centralprocessing unit for performing or executing instructions and one or morememory devices for storing instructions and data. The central processingunit and the memory can be supplemented by, or incorporated in, specialpurpose logic circuitry. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto-optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio or video player, a game console, a Global PositioningSystem (GPS) receiver, or a portable storage device, e.g., a universalserial bus (USB) flash drive, to name just a few.

Computer-readable media suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's device in response to requests received from the web browser.Also, a computer can interact with a user by sending text messages orother forms of message to a personal device, e.g., a smartphone that isrunning a messaging application, and receiving responsive messages fromthe user in return.

Data processing apparatus for implementing machine learning models canalso include, for example, special-purpose hardware accelerator unitsfor processing common and compute-intensive parts of machine learningtraining or production, i.e., inference, workloads.

Machine learning models can be implemented and deployed using a machinelearning framework, e.g., a TensorFlow framework.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back-end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front-end component, e.g., aclient computer having a graphical user interface, a web browser, or anapp through which a user can interact with an implementation of thesubject matter described in this specification, or any combination ofone or more such back-end, middleware, or front-end components. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication, e.g., a communication network. Examples ofcommunication networks include a local area network (LAN) and a widearea network (WAN), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someembodiments, a server transmits data, e.g., an HTML page, to a userdevice, e.g., for purposes of displaying data to and receiving userinput from a user interacting with the device, which acts as a client.Data generated at the user device, e.g., a result of the userinteraction, can be received at the server from the device.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or on the scope of what may be claimed, but rather asdescriptions of features that may be specific to particular embodimentsof particular inventions. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially be claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings and recited inthe claims in a particular order, this should not be understood asrequiring that such operations be performed in the particular ordershown or in sequential order, or that all illustrated operations beperformed, to achieve desirable results. In certain circumstances,multitasking and parallel processing may be advantageous. Moreover, theseparation of various system modules and components in the embodimentsdescribed above should not be understood as requiring such separation inall embodiments, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. As one example, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In some cases, multitasking and parallel processing may beadvantageous.

What is claimed is:
 1. A method comprising: receiving a selection of amedical condition; obtaining data identifying a plurality of brainparcels that are predicted to be relevant to the medical condition;receiving imaging data for a brain of a subject; processing the imagingdata for the brain of the subject to determine a respective featurescore for each of the plurality of brain parcels that are predicted tobe relevant to the medical condition; determining, for each of theplurality of brain parcels that are predicted to be relevant to themedical condition, a relative feature score for the brain parcel basedon: (i) the feature score for the brain parcel in the subject, and (ii)a distribution of feature scores for the brain parcel across apopulation of subjects; providing, to a user device of a user, therelative feature scores for the plurality of brain parcels that arepredicted to be relevant to the medical condition as explainability datathat explains the medical condition in the subject; and taking an actionbased on the relative feature scores.
 2. The method of claim 1, whereintaking an action based on the relative feature scores comprises: afterproviding the relative feature scores to the user device of the user,receiving selection data from the user that selects one or more of theplurality of brain parcels that are predicted to be relevant to themedical condition; and taking the action in response to receiving theselection data from the user.
 3. The method of claim 2, wherein takingthe action in response to receiving the selection data from the usercomprises: obtaining, for each selected brain parcel, respectiveanatomical data that defines a volumetric region in the brain of thesubject that corresponds to the selected parcel; and providing, fordisplay on the user device, a visual representation of the anatomicaldata for the selected brain parcels as part of a three-dimensional modelof the brain of the subject.
 4. The method of claim 2, wherein takingthe action in response to receiving the selection data from the usercomprises: exporting data identifying the selected brain parcels.
 5. Themethod of claim 1, wherein for each of the plurality of brain parcelsthat are predicted to be relevant to the medical condition, determiningthe relative feature score for the brain parcel comprises: determiningthe relative feature score for the brain parcel based on a ratio of: (i)the feature score for the brain parcel in the subject, and (ii) ameasure of central tendency of the distribution of feature scores forthe brain parcel across the population of subjects.
 6. The method ofclaim 5, wherein the measure of central tendency is a mean or a median.7. The method of claim 1, wherein the plurality of brain parcels thatare predicted to be relevant to the medical condition have beenidentified by operations comprising: training a machine learning modelto process an input derived from imaging data for a brain of an inputsubject to predict whether the input subject has the medical condition;identifying the plurality of brain parcels that are predicted to berelevant to the medical condition using the machine learning model. 8.The method of claim 7, wherein identifying the plurality of brainparcels that are predicted to be relevant to the medical condition usingthe machine learning model comprises: determining, for each brain parcelin a parcel atlas, a respective importance score for the brain parcelthat measures an impact of the brain parcel on predictions generated bythe machine learning model; and identifying the plurality of brainparcels that are predicted to be relevant to the medical condition usingthe importance scores for the brain parcels in the parcel atlas.
 9. Themethod of claim 8, wherein identifying the plurality of brain parcelsthat are predicted to be relevant to the medical condition using theimportance scores for the brain parcels in the parcel atlas comprises:identifying each brain parcel in the parcel atlas having an importancescore that satisfies a predefined threshold as being relevant to themedical condition.
 10. The method of claim 8, wherein identifying theplurality of brain parcels that are predicted to be relevant to themedical condition using the importance scores for the brain parcels inthe parcel atlas comprises: identifying a predefined number of brainparcels that are associated with the highest importance scores from thebrain parcels in the parcel atlas as being relevant to the medicalcondition.
 11. The method of claim 7, wherein training a machinelearning model to process an input derived from imaging data for a brainof an input subject to predict whether the input subject has the medicalcondition comprises: training the machine learning model on a set oftraining data comprising a plurality of training examples, wherein eachtraining example includes: (i) a training input derived from imagingdata for a brain of a training subject, and (ii) a label definingwhether the training subject has the medical condition.
 12. The methodof claim 7, wherein the machine learning model is configured to processan input that comprises a functional connectivity matrix derived fromfMRI data for the brain of the input subject.
 13. The method of claim 7,wherein the machine learning model is configured to generate an outputthat defines a predicted likelihood that the input subject has themedical condition.
 14. The method of claim 7, wherein the machinelearning model comprises a neural network model.
 15. The method of claim1, wherein the plurality of brain parcels that are predicted to berelevant to the medical condition are a proper subset of a set ofparcels that collectively define a parcel atlas.
 16. The method of claim15, wherein the plurality of brain parcels that are predicted to berelevant to the medical condition comprise fewer than 5% of the set ofparcels that collectively define the parcel atlas.
 17. The method ofclaim 1, wherein the medical condition is a psychiatric condition or abehavioral symptom of a psychiatric condition.
 18. The method of claim1, wherein the imaging data for the brain of the subject comprises fMRIdata, and wherein for each of the plurality of brain parcels that arepredicted to be relevant to the medical condition, the feature score forthe brain parcel characterizes blood flow in the brain parcel in thebrain of the subject.
 19. A system comprising: one or more computers;and one or more storage devices communicatively coupled to the one ormore computers, wherein the one or more storage devices storeinstructions that, when executed by the one or more computers, cause theone or more computers to perform operations comprising: receiving aselection of a medical condition; obtaining data identifying a pluralityof brain parcels that are predicted to be relevant to the medicalcondition; receiving imaging data for a brain of a subject; processingthe imaging data for the brain of the subject to determine a respectivefeature score for each of the plurality of brain parcels that arepredicted to be relevant to the medical condition; determining, for eachof the plurality of brain parcels that are predicted to be relevant tothe medical condition, a relative feature score for the brain parcelbased on: (i) the feature score for the brain parcel in the subject, and(ii) a distribution of feature scores for the brain parcel across apopulation of subjects; providing, to a user device of a user, therelative feature scores for the plurality of brain parcels that arepredicted to be relevant to the medical condition as explainability datathat explains the medical condition in the subject; and taking an actionbased on the relative feature scores.
 20. One or more non-transitorycomputer storage media storing instructions that when executed by one ormore computers cause the one or more computers to perform operationscomprising: receiving a selection of a medical condition; obtaining dataidentifying a plurality of brain parcels that are predicted to berelevant to the medical condition; receiving imaging data for a brain ofa subject; processing the imaging data for the brain of the subject todetermine a respective feature score for each of the plurality of brainparcels that are predicted to be relevant to the medical condition;determining, for each of the plurality of brain parcels that arepredicted to be relevant to the medical condition, a relative featurescore for the brain parcel based on: (i) the feature score for the brainparcel in the subject, and (ii) a distribution of feature scores for thebrain parcel across a population of subjects; providing, to a userdevice of a user, the relative feature scores for the plurality of brainparcels that are predicted to be relevant to the medical condition asexplainability data that explains the medical condition in the subject;and taking an action based on the relative feature scores.
 21. Themethod of claim 1, wherein providing, to the user device of the user,the relative feature scores for the plurality of brain parcels that arepredicted to be relevant to the medical condition as explainability datathat explains the medical condition in the subject comprises: providinga visualization of the brain of the subject to the user, wherein thevisualization shows each of one or more brain parcels in the brain ofsubject having a respective shade.